Separate explainers, predict and explorers in Units

DashAI/back/api/api_v1/endpoints/predict.py

@@ -252,7 +252,6 @@ async def delete_prediction(
 @inject
 async def preview_manual_prediction(
     request: Request,
-    component_registry: "ComponentRegistry" = Depends(lambda: di["component_registry"]),
     session_factory: "sessionmaker" = Depends(lambda: di["session_factory"]),
 ):
     """Run a synchronous manual prediction and return results without persisting.
@@ -335,7 +334,6 @@ async def preview_manual_prediction(
         run_manual_prediction,
         run_id=run_id_int,
         manual_input_data=rows_data,
-        component_registry=component_registry,
         session_factory=session_factory,
     )
     return {"columns": columns, "rows": rows}

DashAI/back/explainability/explainers/contrastive_shap.py

@@ -238,7 +238,10 @@ def fit(
         """
         import shap
 
-        from DashAI.back.explainability.model_input import prepare_model_input
+        from DashAI.back.explainability.model_input import (
+            as_shap_predictor,
+            prepare_model_input,
+        )
 
         x, y = background_dataset
         # SHAP calls the model with perturbed frames, which skip the model
@@ -254,7 +257,7 @@ def fit(
             background_data = shap.sample(background_data, n_samples)
 
         self.explainer = shap.KernelExplainer(
-            model=self.model.predict,
+            model=as_shap_predictor(self.model),
             data=background_data,
             feature_names=feature_names,
         )

DashAI/back/explainability/explainers/kernel_shap.py

@@ -333,7 +333,10 @@ def fit(
         """
         sample_background_data = bool(sample_background_data)
 
-        from DashAI.back.explainability.model_input import prepare_model_input
+        from DashAI.back.explainability.model_input import (
+            as_shap_predictor,
+            prepare_model_input,
+        )
 
         x, y = background_dataset
 
@@ -365,7 +368,7 @@ def fit(
         import shap
 
         self.explainer = shap.KernelExplainer(
-            model=self.model.predict,
+            model=as_shap_predictor(self.model),
             data=background_data,
             feature_names=feature_names,
             link=self.link,

DashAI/back/explainability/explainers/regression_kernel_shap.py

@@ -179,7 +179,10 @@ def fit(
         """
         import shap
 
-        from DashAI.back.explainability.model_input import prepare_model_input
+        from DashAI.back.explainability.model_input import (
+            as_shap_predictor,
+            prepare_model_input,
+        )
 
         x, y = background_dataset
         # SHAP calls the model with perturbed frames, which skip the model
@@ -195,7 +198,7 @@ def fit(
             background_data = shap.sample(background_data, n_samples)
 
         self.explainer = shap.KernelExplainer(
-            model=self.model.predict,
+            model=as_shap_predictor(self.model),
             data=background_data,
             feature_names=feature_names,
         )

DashAI/back/explainability/model_input.py

@@ -14,12 +14,50 @@
 that both live in the same space.
 """
 
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Callable
 
 if TYPE_CHECKING:
     from DashAI.back.dataloaders.classes.dashai_dataset import DashAIDataset
 
 
+def as_shap_predictor(model: Any) -> Callable:
+    """Wrap ``model.predict`` so SHAP receives a plain function, not a method.
+
+    SHAP suppresses scikit-learn's "X does not have valid feature names"
+    warning by blanking ``feature_names_in_`` on whatever object the callable
+    is bound to (``shap.utils._legacy.convert_to_model``). It reaches that
+    object through ``__self__``, so it only does this when handed a *bound
+    method*, and it assumes the attribute is writable.
+
+    That assumption does not hold for every model DashAI ships: the LightGBM
+    and XGBoost wrappers inherit ``feature_names_in_`` from their upstream
+    estimator as a read-only ``property``, so the assignment raises
+    ``AttributeError: property 'feature_names_in_' ... has no setter`` and the
+    explanation fails before it starts.
+
+    Handing over a plain closure instead leaves ``__self__`` absent, so SHAP
+    skips that step entirely — a function is SHAP's primary documented
+    interface for ``model``. The only thing lost is the suppression of a
+    cosmetic scikit-learn warning.
+
+    Parameters
+    ----------
+    model : Any
+        The trained model being explained.
+
+    Returns
+    -------
+    Callable
+        A one-argument function calling ``model.predict`` positionally, the
+        same way SHAP calls it today.
+    """
+
+    def predict(x):
+        return model.predict(x)
+
+    return predict
+
+
 def prepare_model_input(model: Any, dataset: "DashAIDataset") -> "DashAIDataset":
     """Apply the model's own input preprocessing to a dataset.
 

DashAI/back/initial_components.py

@@ -351,14 +351,31 @@
 
 # Units
 from DashAI.back.units.apply_converter_unit import ApplyConverterUnit
+from DashAI.back.units.build_global_explainer_unit import BuildGlobalExplainerUnit
+from DashAI.back.units.build_local_explainer_unit import BuildLocalExplainerUnit
+from DashAI.back.units.build_manual_input_unit import BuildManualInputUnit
 from DashAI.back.units.build_model_unit import BuildModelUnit
 from DashAI.back.units.evaluate_model_unit import EvaluateModelUnit
 from DashAI.back.units.fit_converter_unit import FitConverterUnit
 from DashAI.back.units.fit_model_unit import FitModelUnit
+from DashAI.back.units.generate_global_explanation_unit import (
+    GenerateGlobalExplanationUnit,
+)
+from DashAI.back.units.generate_local_explanation_unit import (
+    GenerateLocalExplanationUnit,
+)
 from DashAI.back.units.load_dataset_unit import LoadDatasetUnit
+from DashAI.back.units.load_run_model_unit import LoadRunModelUnit
+from DashAI.back.units.load_trained_model_unit import LoadTrainedModelUnit
+from DashAI.back.units.load_training_dataset_unit import LoadTrainingDatasetUnit
+from DashAI.back.units.predict_unit import PredictUnit
 from DashAI.back.units.prepare_and_split_unit import PrepareAndSplitUnit
+from DashAI.back.units.prepare_explanation_data_unit import PrepareExplanationDataUnit
+from DashAI.back.units.run_exploration_unit import RunExplorationUnit
 from DashAI.back.units.save_dataset_unit import SaveDatasetUnit
+from DashAI.back.units.save_exploration_unit import SaveExplorationUnit
 from DashAI.back.units.save_model_unit import SaveModelUnit
+from DashAI.back.units.save_prediction_unit import SavePredictionUnit
 from DashAI.back.units.transform_dataset_unit import TransformDatasetUnit
 
 logging.basicConfig(level=logging.DEBUG)
@@ -536,6 +553,19 @@ def get_initial_components():
         FitConverterUnit,
         TransformDatasetUnit,
         SaveDatasetUnit,
+        RunExplorationUnit,
+        SaveExplorationUnit,
+        LoadTrainedModelUnit,
+        LoadTrainingDatasetUnit,
+        BuildManualInputUnit,
+        PredictUnit,
+        SavePredictionUnit,
+        LoadRunModelUnit,
+        BuildGlobalExplainerUnit,
+        BuildLocalExplainerUnit,
+        PrepareExplanationDataUnit,
+        GenerateGlobalExplanationUnit,
+        GenerateLocalExplanationUnit,
         # Explainers
         ContrastiveShap,
         DiceCounterfactual,